docstr: Check if initial pipeline can be fitted on a presented data
  
  :param pipeline: pipeline for checking
  :param pipelines_cache: Cache manager for fitted models, optional.
  :param preprocessing_cache: Cache manager for optional preprocessing encoders and imputers, optional.
  :param eval_n_jobs: number of jobs to fit the initial pipeline
docstr_tok: ["Check","if","initial","pipeline","can","be","fitted","on","a","presented","data",":","param","pipeline",":","pipeline","for","checking",":","param","pipelines_cache",":","Cache","manager","for","fitted","models",",","optional",".",":","param","preprocessing_cache",":","Cache","manager","for","optional","preprocessing","encoders","and","imputers",",","optional",".",":","param","eval_n_jobs",":","number","of","jobs","to","fit","the","initial","pipeline"]
code: def fit_assumption_and_check_correctness(
      self,
      pipeline: Pipeline,
      pipelines_cache: Optional[OperationsCache] = None,
      preprocessing_cache: Optional[PreprocessingCache] = None,
      eval_n_jobs: int = -1,
  ) -> Pipeline:
      """
      Check if initial pipeline can be fitted on a presented data
  
      :param pipeline: pipeline for checking
      :param pipelines_cache: Cache manager for fitted models, optional.
      :param preprocessing_cache: Cache manager for optional preprocessing encoders and imputers, optional.
      :param eval_n_jobs: number of jobs to fit the initial pipeline
      """
      try:
          data_train, data_test = train_test_data_setup(self.data)
          self.log.info("Initial pipeline fitting started")
          # load preprocessing
          pipeline.try_load_from_cache(
              pipelines_cache, preprocessing_cache
          )
          pipeline.fit(data_train, n_jobs=eval_n_jobs)
  
          if pipelines_cache is not None:
              pipelines_cache.save_pipeline(pipeline)
          if preprocessing_cache is not None:
              preprocessing_cache.add_preprocessor(pipeline)
  
          pipeline.predict(data_test)
          self.log.info("Initial pipeline was fitted successfully")
  
          MemoryAnalytics.log(
              self.log,
              additional_info="fitting of the initial pipeline",
              logging_level=45,
          )  # message logging level
  
      except Exception as ex:
          self._raise_evaluating_exception(ex)
      return pipeline
  
file_path: FEDOT/fedot/api/api_utils/assumptions/assumptions_handler.py
imports: import traceback
  from typing import List, Optional, Union
  from golem.core.log import default_log
  from fedot.api.api_utils.assumptions.assumptions_builder import AssumptionsBuilder
  from fedot.api.api_utils.presets import change_preset_based_on_initial_fit
  from fedot.api.time import ApiTime
  from fedot.core.caching.pipelines_cache import OperationsCache
  from fedot.core.caching.preprocessing_cache import PreprocessingCache
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.utilities.memory import MemoryAnalytics
label_desc: Use image node_id 3 for calling the AssumptionsHandler obj's underlying member method code with example usage: obj.fit_assumption_and_check_correctness(pipeline, pipelines_cache, preprocessing_cache, eval_n_jobs) and returns: pipeline